mirror of
https://github.com/qdrant/landing_page.git
synced 2026-09-28 23:48:31 +02:00
docs: New Go SDK snippets (#1149)
This commit is contained in:
Anush
@@ -282,6 +282,34 @@ await client.CreateCollectionAsync(
|
||||
);
|
||||
```
|
||||
|
||||
```go
|
||||
import (
|
||||
"context"
|
||||
|
||||
"github.com/qdrant/go-client/qdrant"
|
||||
)
|
||||
|
||||
client, err := qdrant.NewClient(&qdrant.Config{
|
||||
Host: "localhost",
|
||||
Port: 6334,
|
||||
})
|
||||
|
||||
client.CreateCollection(context.Background(), &qdrant.CreateCollection{
|
||||
CollectionName: "{collection_name}",
|
||||
VectorsConfig: qdrant.NewVectorsConfig(&qdrant.VectorParams{
|
||||
Size: 768,
|
||||
Distance: qdrant.Distance_Cosine,
|
||||
}),
|
||||
QuantizationConfig: qdrant.NewQuantizationScalar(
|
||||
&qdrant.ScalarQuantization{
|
||||
Type: qdrant.QuantizationType_Int8,
|
||||
Quantile: qdrant.PtrOf(float32(0.99)),
|
||||
AlwaysRam: qdrant.PtrOf(true),
|
||||
},
|
||||
),
|
||||
})
|
||||
```
|
||||
|
||||
There are 3 parameters that you can specify in the `quantization_config` section:
|
||||
|
||||
`type` - the type of the quantized vector components. Currently, Qdrant supports only `int8`.
|
||||
@@ -418,6 +446,32 @@ await client.CreateCollectionAsync(
|
||||
);
|
||||
```
|
||||
|
||||
```go
|
||||
import (
|
||||
"context"
|
||||
|
||||
"github.com/qdrant/go-client/qdrant"
|
||||
)
|
||||
|
||||
client, err := qdrant.NewClient(&qdrant.Config{
|
||||
Host: "localhost",
|
||||
Port: 6334,
|
||||
})
|
||||
|
||||
client.CreateCollection(context.Background(), &qdrant.CreateCollection{
|
||||
CollectionName: "{collection_name}",
|
||||
VectorsConfig: qdrant.NewVectorsConfig(&qdrant.VectorParams{
|
||||
Size: 1536,
|
||||
Distance: qdrant.Distance_Cosine,
|
||||
}),
|
||||
QuantizationConfig: qdrant.NewQuantizationBinary(
|
||||
&qdrant.BinaryQuantization{
|
||||
AlwaysRam: qdrant.PtrOf(true),
|
||||
},
|
||||
),
|
||||
})
|
||||
```
|
||||
|
||||
`always_ram` - whether to keep quantized vectors always cached in RAM or not. By default, quantized vectors are loaded in the same way as the original vectors.
|
||||
However, in some setups you might want to keep quantized vectors in RAM to speed up the search process.
|
||||
|
||||
@@ -553,6 +607,33 @@ await client.CreateCollectionAsync(
|
||||
);
|
||||
```
|
||||
|
||||
```go
|
||||
import (
|
||||
"context"
|
||||
|
||||
"github.com/qdrant/go-client/qdrant"
|
||||
)
|
||||
|
||||
client, err := qdrant.NewClient(&qdrant.Config{
|
||||
Host: "localhost",
|
||||
Port: 6334,
|
||||
})
|
||||
|
||||
client.CreateCollection(context.Background(), &qdrant.CreateCollection{
|
||||
CollectionName: "{collection_name}",
|
||||
VectorsConfig: qdrant.NewVectorsConfig(&qdrant.VectorParams{
|
||||
Size: 768,
|
||||
Distance: qdrant.Distance_Cosine,
|
||||
}),
|
||||
QuantizationConfig: qdrant.NewQuantizationProduct(
|
||||
&qdrant.ProductQuantization{
|
||||
Compression: qdrant.CompressionRatio_x16,
|
||||
AlwaysRam: qdrant.PtrOf(true),
|
||||
},
|
||||
),
|
||||
})
|
||||
```
|
||||
|
||||
There are two parameters that you can specify in the `quantization_config` section:
|
||||
|
||||
`compression` - compression ratio.
|
||||
@@ -697,6 +778,31 @@ await client.QueryAsync(
|
||||
);
|
||||
```
|
||||
|
||||
```go
|
||||
import (
|
||||
"context"
|
||||
|
||||
"github.com/qdrant/go-client/qdrant"
|
||||
)
|
||||
|
||||
client, err := qdrant.NewClient(&qdrant.Config{
|
||||
Host: "localhost",
|
||||
Port: 6334,
|
||||
})
|
||||
|
||||
client.Query(context.Background(), &qdrant.QueryPoints{
|
||||
CollectionName: "{collection_name}",
|
||||
Query: qdrant.NewQuery(0.2, 0.1, 0.9, 0.7),
|
||||
Params: &qdrant.SearchParams{
|
||||
Quantization: &qdrant.QuantizationSearchParams{
|
||||
Ignore: qdrant.PtrOf(false),
|
||||
Rescore: qdrant.PtrOf(true),
|
||||
Oversampling: qdrant.PtrOf(2.0),
|
||||
},
|
||||
},
|
||||
})
|
||||
```
|
||||
|
||||
`ignore` - Toggle whether to ignore quantized vectors during the search process. By default, Qdrant will use quantized vectors if they are available.
|
||||
|
||||
`rescore` - Having the original vectors available, Qdrant can re-evaluate top-k search results using the original vectors.
|
||||
@@ -827,6 +933,29 @@ await client.QueryAsync(
|
||||
);
|
||||
```
|
||||
|
||||
```go
|
||||
import (
|
||||
"context"
|
||||
|
||||
"github.com/qdrant/go-client/qdrant"
|
||||
)
|
||||
|
||||
client, err := qdrant.NewClient(&qdrant.Config{
|
||||
Host: "localhost",
|
||||
Port: 6334,
|
||||
})
|
||||
|
||||
client.Query(context.Background(), &qdrant.QueryPoints{
|
||||
CollectionName: "{collection_name}",
|
||||
Query: qdrant.NewQuery(0.2, 0.1, 0.9, 0.7),
|
||||
Params: &qdrant.SearchParams{
|
||||
Quantization: &qdrant.QuantizationSearchParams{
|
||||
Ignore: qdrant.PtrOf(false),
|
||||
},
|
||||
},
|
||||
})
|
||||
```
|
||||
|
||||
- **Adjust the quantile parameter**: The quantile parameter in scalar quantization determines the quantization bounds.
|
||||
By setting it to a value lower than 1.0, you can exclude extreme values (outliers) from the quantization bounds.
|
||||
For example, if you set the quantile to 0.99, 1% of the extreme values will be excluded.
|
||||
@@ -978,6 +1107,34 @@ await client.CreateCollectionAsync(
|
||||
);
|
||||
```
|
||||
|
||||
```go
|
||||
import (
|
||||
"context"
|
||||
|
||||
"github.com/qdrant/go-client/qdrant"
|
||||
)
|
||||
|
||||
client, err := qdrant.NewClient(&qdrant.Config{
|
||||
Host: "localhost",
|
||||
Port: 6334,
|
||||
})
|
||||
|
||||
client.CreateCollection(context.Background(), &qdrant.CreateCollection{
|
||||
CollectionName: "{collection_name}",
|
||||
VectorsConfig: qdrant.NewVectorsConfig(&qdrant.VectorParams{
|
||||
Size: 768,
|
||||
Distance: qdrant.Distance_Cosine,
|
||||
OnDisk: qdrant.PtrOf(true),
|
||||
}),
|
||||
QuantizationConfig: qdrant.NewQuantizationScalar(
|
||||
&qdrant.ScalarQuantization{
|
||||
Type: qdrant.QuantizationType_Int8,
|
||||
AlwaysRam: qdrant.PtrOf(true),
|
||||
},
|
||||
),
|
||||
})
|
||||
```
|
||||
|
||||
In this scenario, the number of disk reads may play a significant role in the search speed.
|
||||
In a system with high disk latency, the re-scoring step may become a bottleneck.
|
||||
|
||||
@@ -1089,6 +1246,29 @@ await client.QueryAsync(
|
||||
);
|
||||
```
|
||||
|
||||
```go
|
||||
import (
|
||||
"context"
|
||||
|
||||
"github.com/qdrant/go-client/qdrant"
|
||||
)
|
||||
|
||||
client, err := qdrant.NewClient(&qdrant.Config{
|
||||
Host: "localhost",
|
||||
Port: 6334,
|
||||
})
|
||||
|
||||
client.Query(context.Background(), &qdrant.QueryPoints{
|
||||
CollectionName: "{collection_name}",
|
||||
Query: qdrant.NewQuery(0.2, 0.1, 0.9, 0.7),
|
||||
Params: &qdrant.SearchParams{
|
||||
Quantization: &qdrant.QuantizationSearchParams{
|
||||
Rescore: qdrant.PtrOf(false),
|
||||
},
|
||||
},
|
||||
})
|
||||
```
|
||||
|
||||
- **All on Disk** - all vectors, original and quantized, are stored on disk. This mode allows to achieve the smallest memory footprint, but at the cost of the search speed.
|
||||
|
||||
It is recommended to use this mode if you have a large collection and fast storage (e.g. SSD or NVMe).
|
||||
@@ -1226,3 +1406,31 @@ await client.CreateCollectionAsync(
|
||||
}
|
||||
);
|
||||
```
|
||||
|
||||
```go
|
||||
import (
|
||||
"context"
|
||||
|
||||
"github.com/qdrant/go-client/qdrant"
|
||||
)
|
||||
|
||||
client, err := qdrant.NewClient(&qdrant.Config{
|
||||
Host: "localhost",
|
||||
Port: 6334,
|
||||
})
|
||||
|
||||
client.CreateCollection(context.Background(), &qdrant.CreateCollection{
|
||||
CollectionName: "{collection_name}",
|
||||
VectorsConfig: qdrant.NewVectorsConfig(&qdrant.VectorParams{
|
||||
Size: 768,
|
||||
Distance: qdrant.Distance_Cosine,
|
||||
OnDisk: qdrant.PtrOf(true),
|
||||
}),
|
||||
QuantizationConfig: qdrant.NewQuantizationScalar(
|
||||
&qdrant.ScalarQuantization{
|
||||
Type: qdrant.QuantizationType_Int8,
|
||||
AlwaysRam: qdrant.PtrOf(false),
|
||||
},
|
||||
),
|
||||
})
|
||||
```
|
||||
|
||||
Reference in New Issue
Block a user